Durvalumab and guadecitabine in advanced clear cell renal cell carcinoma: results from the phase Ib/II study BTCRC-GU16-043.

Epigenetic modulation is well established in hematologic malignancies but to a lesser degree in solid tumors. Here we report the results of a phase Ib/II study of guadecitabine and durvalumab in advanced clear cell renal cell carcinoma (ccRCC; NCT03308396). Patients received guadecitabine (starting at 60 mg/m2 subcutaneously on days 1-5 with de-escalation to 45 mg/m2 in case of dose limiting toxicity) with durvalumab (1500 mg intravenously on day 8). The study enrolled 57 patients, 6 in phase Ib with safety being the primary objective and 51in phase II, comprising 2 cohorts: 36 patients in Cohort 1 were treatment naive to checkpoint inhibitors (CPI) with 0-1 prior therapies and 15 patients in Cohort 2 were treated with up to two prior systemic therapies including one CPI. The combination of guadecitabine 45 mg/m2 with durvalumab 1500 mg was deemed safe. The primary objective of overall response rate (ORR) in cohort 1 was 22%. Sixteen patients (44%) experienced stable disease (SD). Secondary objectives included overall survival (OS), duration of response, progression-free survival (PFS), clinical benefit rate, and safety as well as ORR for Cohort 2. Median PFS for cohort 1 and cohort 2 were 14.26 and 3.91 months respectively. Median OS was not reached. In cohort 2, one patient achieved a partial response and 60% achieved SD. Asymptomatic neutropenia was the most common adverse event. Even though the trial did not meet the primary objective in cohort 1, the tolerability and PFS signal in CPI naive patients are worth further investigation.

SpaceX: gene co-expression network estimation for spatial transcriptomics.

MOTIVATION: The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains. RESULTS: We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data. AVAILABILITY AND IMPLEMENTATION: The SpaceX R-package is available at github.com/bayesrx/SpaceX. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.